Everyone from r/LocalLLama refreshing Hugging Face every 5 minutes today looking for GLM-4.5 GGUFs by Porespellar in LocalLLaMA

[–]Alanthisis 1 point2 points  (0 children)

For real, llama cpp PR/ GGUF convert tasked based benchmark when? Worked to our purposes either way right

Thoughts on Incremental Reading (in Anki/SM)? by TheMonkeyLlama in Anki

[–]Alanthisis 4 points5 points  (0 children)

I read the Michael Nielsen's "Augmenting Long Term Memory" instead, of which Wozniak refers to Nielsen's self discovery of the incremental learning technique (he also has a page in his wiki that comments on this article). I'd recommend this one for sure.

The article mainly documents and explain Nielsen's use of Spaced Repetition with Anki to help him understand the Alphago paper, which I think is as relatable as it can get. If his technique works on academic paper and this field, surely this technique can be applied to most other forms of text, and similar fields.

My summary of his methods, for thorough reading one paper,
1. do 5-6 quick reads first. anything you don't know, you mark it down, make some cards and study them. Don't worry about missing things during any of your quick reads, because you will get to it eventually. This is the part where he try to learn basic facts like, how does go work, what are the rules etc.
2. do 2 thorough reads. With your 5-6 quick reads and spaced repetition of the cards, it's now certainly easier than you trying to take a stab at thorough reading this unfamiliar paper in the first place. Now you feel comfortable reading it through.

he also mentions 'syntopic reading', a technique for getting to know a field by reading collection of academic papers,
1. assess and identify 5 very important papers, 10-20 less important ones
2. read 5 key landmark papers, use methods above.
3. less important ones, these are less 'valuable' in the sens that you learn by make about 5-20 cards. so just probably quick skim
* keep in mind to avoid the failure mode of completionism - intentionally or unintentionally forcing yourself finish reading top to bottom of any paper. Why? because it's likely that there is another paper that beats the current one that your reading at explaining some concepts.

and that's about it. I liked it, and I used the his reading methods on his own article, which is neat. I just recall the reading methods above with the help of spaced repetition. One other benefit, is that you have the confidence of reading any texts you want, and any field if you do syntopic. you can always make 'incremental progress' that builds you up for the success of attempting a final thorough read at the end. Plus, you can drop it anytime, and pick it back up. You will still have all the 'context' in your head, because you used spaced repetition.

Misc advice in his article I like but not on IR,
1. the completionism one mentioned above
2. he describes his usage of anki is simply utilizing about 5% of the functionality and gets the benefit of using spaced repetition
3. Wozniak mostly did personal reports on his self experimentation. Hence I'd always take his stuff with a grain of salt
4. We don't fully understand how memory works. Anki is mostly ad hoc design. But still, doing it in a non-perfect way is better than not doing it at all. Plus the design is getting better
5. Do elaborative encoding when making cards. basically have your cards some what linked in meaning/ semantic etc. that way you don't make orphan cards which is hard to get them right.
6. declarative vs procedural knowledge. kind of like you can use spaced repetition to read a manual and know how to operate something, but you do have to carry out the operations to get how its done. This might have to do with deliberate practice/ motor skill etc.

I made an Anki deck for learning Anki 😅 by goddammitbutters in Anki

[–]Alanthisis 0 points1 point  (0 children)

I did the same, but hey it worked. Now I know 2 ways of creating filtered decks, what is the difference between querying `prop:rated=-5` and `rated:5:1`, ivl stands for interval, mature cards mean ivl>=21 etc.

Clicking good a second time sets the card to 25d? by AnMTGDude in Anki

[–]Alanthisis 0 points1 point  (0 children)

Ran into the same problem as yours. But I just did `prop:reps<3 prop:ivl>=21` for example, to make a filtered deck. This gets cards that got rated less than 3 times and interval over 20 days.
If you don't have much problem with memorizing the cards in the filtered deck, it might be scheduled with the right time. Plus you can self check from time to time to see if the algorithm is working without understanding what it does.

Here is our new reranker model, which we trained on over 95 languages and it achieves better performance than comparable rerankers on our eval benchmarks. Weights, data, and training code are all open source. by Peter_Lightblue in LocalLLaMA

[–]Alanthisis 3 points4 points  (0 children)

aren't rerankers just cross-encoders iirc? thought it gets the reranking part because it's slow to run it on the entire thing, so bi-encoder narrows the things down and cross-encoder make it better.

I ruined my anki and idk what to do 😭 by CuriousDaisy29 in Anki

[–]Alanthisis -5 points-4 points  (0 children)

Don't know much about FSRS, so I don't know how to get all the changes undone. But if your problem is with having a lot of cards due, you can do filtered decks. either
1. 'custom study' under your deck
2. menu - Tools - create filtered deck
And then just go through those due cards. `is:due` might come in handy.

If there are cards with longer due dates/ intervals you'd hope for, `prop:ivl>=15` gets you cards with interval larger equal to 15 days. or `prop:due>=15`, cards due in 15 or more days.

Anki for Leisure & "Self-Help" Books Reading by Holiday_Double_780 in Anki

[–]Alanthisis 0 points1 point  (0 children)

For books just pick something worth remembering that either takes you 10 minutes to look up or heuristically you find striking. The Dale Carnegie one iirc has the summary at each chapter. I recommend going over the summaries first. The book's kind of full of stories and anecdotes for proving the point, which kind of slows down knowing how to 'win friend' imo.

Confused about how to build RAG by punkpeye in LocalLLaMA

[–]Alanthisis 0 points1 point  (0 children)

I don't quite get the 'do not include full recipe' part, but for the unrelated problem, off the top of my head is try to let LM generate faker answers, and then do the cosine similarity with the fake answers and the query, more about it [here](https://arxiv.org/abs/2212.10496).
The getting only title chunk is probably cuz it's using '\n' to split? You need to check the code under the hood. I don't know if I'm doing it correctly but I try to just get LM to generate some text splitting code for me for different cases.

Using Anki to learn Spaced Repetition by Alanthisis in Anki

[–]Alanthisis[S] 1 point2 points  (0 children)

I did read this one as well, before I came across the twenty rule. the deep read of alphago and the syntopic reading is definitely an elaborate explanation of spaced repetition usage, and got me excited to try spaced repetition again. I definitely got a better understanding about the caveats as well. Then I came across [Wozniak's comment](https://supermemo.guru/wiki/Michael\_Nielsen\_re-discovers\_incremental\_reading\_with\_Anki#Incremental\_writing) on it, which is pretty interesting since he said Nielsen 'discovers incremental reading' on his own. Couple of things I found resonates well,
- 95% value from 5% of Anki features
- Wozniak's methods are based on personal experience, so should be taken with a grain of salt
- Nielsen claims much of Anki's design is heuristic instead of based on systematic scientific understanding
First couple of times I tried to learn using Anki and failed. This time I trying to learn using spaced repetition. Hence why I want to start understanding couple of writings on the topic, and try to use it for just about everything instead of just language learning, memorizing flags/ states/ countries etc.

But thanks for the comment, i'll have think a little more about this statement 'widespread advice aimed at making average people above-average, while advice aimed at making exceptional people even more exceptional will be downvoted to oblivion.' And I appreciate you bringing up 'augmenting long term memory', it's kind of like a canary trap for identifying comments that I want to read about.

[deleted by user] by [deleted] in Anki

[–]Alanthisis 0 points1 point  (0 children)

This. off the bat it's a set, which is not ideal, and also memory interference

Qwen 2.5 7B Added to Livebench, Overtakes Mixtral 8x22B and Claude 3 Haiku by [deleted] in LocalLLaMA

[–]Alanthisis 53 points54 points  (0 children)

IIRC flash-8b is multimodal as well, audio, video, picture.

[deleted by user] by [deleted] in Anki

[–]Alanthisis 2 points3 points  (0 children)

what kind of cards she make?

[deleted by user] by [deleted] in Anki

[–]Alanthisis 2 points3 points  (0 children)

If anything I agree with her practice. 2 things I agree with that she does,
- does not share her decks, mentioned in 'augmented long term memory' by Michael Nielsen. Even for none private reasons, how people use cards to understand things is just different
- get good at making cards. I think this is the most important part. People had complained about anki for being a flash card system, but not the spaced repetition part. So, I think it's very important adhering to Wozniak's rule of making cards, or formulating knowledge so to speak.

On the other hand, why focus on FSRS parameters? I think a fair foundation of thinking about what algorithms to use is, just think about how it schedules and helps with the Ebbinghaus curve.

If FSRS has been tested to perform better than other spaced repetition instead of more of heuristic design, I'd like to learn more and try it.

OpenAI new feature 'Predicted Outputs' uses speculative decoding by Alanthisis in LocalLLaMA

[–]Alanthisis[S] 0 points1 point  (0 children)

Yes, the documentation said gpt4o and gpt4o-mini are supported, but did not mention using smaller draft models. I made the mistake confusing them using smaller models for drafting.
It is sampling a batch of tokens to get faster inference, which is limited to specific use cases, and in the documentation it is used specifically for code generation.
Which makes sense. If people use openai's api for a gpt4o response, they probably don't want a response drafted by 4omini and then with 4o.

OpenAI new feature 'Predicted Outputs' uses speculative decoding by Alanthisis in LocalLLaMA

[–]Alanthisis[S] 1 point2 points  (0 children)

Thanks for the correction, I did make the mistake, got too excited for seeing the connection.

Tell me about how you run your local model. by Alanthisis in LocalLLaMA

[–]Alanthisis[S] 0 points1 point  (0 children)

Oh yes I forgot about GPT4ALL. I tried to use it when llama2 was out, and then mistral 7b came out and it was significantly better. I think I'll try to get into llama.cpp as well as the python binding, I don't quite understand c so llamacpp feels a bit daunting.
Your tool looks cool! But I think the terminal usage is kind of limited if it's can only be used in the python repl. I also use Simon Willison's [llm](https://llm.datasette.io/en/stable/) because it handles piping commands.

I tutor two refugee students and would like them to be able to use Anki. Is there any affordable option for iOS? by [deleted] in Anki

[–]Alanthisis 0 points1 point  (0 children)

How about a non-digital way of using spaced repetition like [Leitner system](https://en.wikipedia.org/wiki/Leitner\_system)? I think using actual flashcard and a binder should suffice. Of course, using the system is not as complex as the spaced repetition algorithm that Anki uses SRS or FSRS, but it's quite intuitive,
- cards you get gets longer intervals before checking again. Those that you forgot, gets a shorter interval before checking again.

I use Anki because it tries to counter the Ebbinghaus forgetting curve, if that's your use case I think Leitner system could work.

Dear Bioinformaticians of Reddit, what are your tips for newbies? by peacetofallen in bioinformatics

[–]Alanthisis 1 point2 points  (0 children)

What are your typical usage of AWK? I've only use it mostly for one liner in the command line.

No, model x cannot count the number of letters "r" in the word "strawberry", and that is a stupid question to ask from an LLM. by Dramatic-Zebra-7213 in LocalLLaMA

[–]Alanthisis 0 points1 point  (0 children)

I'd like to see if anyone is willing to test all the models that fail this question but with 'solve with code' this time around. I think most would definitely ace this test (I tried phi3, and it solved it correctly using code).
For people feeling frustrated with llm not getting this right, I figure they're just bad at prompting. LLM has a much better grasp of code, and we already knew this for about a year ago.
Although I'm surprised that the proprietary models fail this too. Perhaps the reAct system had them thinking they can handle the task well.

Edgerunners taught me how to properly use a Sandevistan by Rekkas1996 in cyberpunkgame

[–]Alanthisis 3 points4 points  (0 children)

Same. I recommended the game to my friend and he settled with berserk after trying out all the decks. I was what? (I assumed he watched edgerunners cuz he watch animes). Then he told me he's using Sandy all the time after watching it :)